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Knowledge graphs store facts using relations between two entities. In this work, we address the question of link prediction in …
We study whether a neural network optimizes to the same, linearly connected minimum under different samples of SGD noise (e.g., random …
The CLEVR dataset of natural-looking questions about 3D-rendered scenes has recently received much attention from the research …
The fields of artificial intelligence and neuroscience have a long history of fertile bi-directional interactions. On the one hand, …
We introduce a machine learning workflow to search for, identify, and meaningfully triage videos and images of munitions, weapons, and …
Recent advances have made it possible to create deep complex-valued neural networks. Despite this progress, the potential power of …
Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here …
We propose a Class-Based Styling method (CBS) that can map different styles for different object classes in real-time. CBS achieves …
Compositional Pattern Producing Networks (CPPNs) are differentiable networks that independently map (x, y) pixel coordinates to (r, g, …
In this work, we improve upon the stepwise analysis of noisy iterative learning algorithms initiated by Pensia, Jog, and Loh (2018) and …